class: center, middle, inverse, title-slide .title[ #
Predicting Covid-19 outbreaks using multi-layer centrality measures
] .subtitle[ ##
Presentation of the Research Report
] .author[ ### Christine Hedde - von Westernhagen ] .date[ ### 14-12-2022
Program MSBBSS, Department of Methodology and Statistics, Utrecht University
Supervisors: Javier Garcia-Bernardo, Ayoub Bagheri
] --- class: center, middle, inverse # Let's break it down: --- class: center, middle, inverse # **Predicting Covid-19 outbreaks** <span style = "color:darkslategray";> using multi-layer centrality measures </span> --- class: middle .pull-left[.center[ <img src="data:image/png;base64,#img/net_animation.gif" width="100%" /> ]] ### <p style="margin:250px 0px 0px 550px"> Model spread to inform policy decisions! </p> --- class: center, middle, inverse # <span style = "color:darkslategray";> Predicting Covid-19 outbreaks using </span> **multi-layer** <span style = "color:darkslategray";> centrality measures </span> --- class: left, top ### Our lives are multi-layered. <img src="img/3dplot_dummy.png" style="height:450px; width:700px; object-fit:cover; border:5px white; margin:10px 0px 15px 30px"> <p style="margin:-400px 0px 0px 800px"> <b> CBS micro-data </b> allows to construct multi-layer network data set. <br><br> Micro-data can be linked to <b> PCR test data. </b> <br><br> Analyses conducted on regional <b> sample </b> of elementary schools. </p> --- class: center, middle, inverse # <span style = "color:darkslategray";> Predicting Covid-19 outbreaks using multi-layer </span> **centrality measures** --- class: center, left ### Do you have... <p style="font-size:25px; text-align:left"> 👩👴👶👦...many close relatives? <br><br> 👩👴👶👦 👭 👧👴👵👦...many close relatives and also a partner with a big family? <br><br> ✈️🌍 👩👴👶👦...many close relatives, but they live in a different place? <br> </p> <br> ## **Then you are central!** -- <br> ## ... and probably a super-spreader. --- class: center, middle, inverse # **Single-layer centrality <br> ≠ <br> Multi-layer centrality ** --- class: left, middle <figure> <img src="img/multilayer_dedomenico_edit.png" style="width:1500px; margin:35px 0px -10px 0px"> <figcaption style="font-size:15px"> <i>De Domenico et al. 2015, Supplementary Figure 1</i> </figcaption> </figure> <br><br> ---- <p style="font-size:15px">De Domenico, M., Solé-Ribalta, A., Omodei, E., Gómez, S., & Arenas, A. (2015). Ranking in interconnected multilayer networks reveals versatile nodes. Nature Communications, 6(1), 6868. https://doi.org/10.1038/ncomms7868 </p> --- class: left, top, inverse ## **Bringing it all together:** <br> > ### In single-layer networks, a relevant indicator of spreading behavior has shown to be the **centrality** of a node. > ### Several studies have used **multi-layer** networks in modeling the spread of Covid. > ### **None** of them have included multi-layer centrality measures that adequately **account for the complex network structure**. --- class: center, middle, inverse # **Preliminary Results** --- class: center ### *Ranking nodes*: Single-layer vs. Multi-layer centrality <img src="img/sankeyplot_dummy.png" style=" width:750px; object-fit:cover; border:5px white; margin:0px 0px 0px 0px"> --- class: center, middle, inverse # **Conclusion** --- class: top, left .pull-left[ ## What we know **so far:** ### Spreading properties of single-layer and multi-layer networks differ - Preliminary results **support** that. ### Multi-layer centrality measures account for complex structure (De Domenico et al. 2015). ] -- .pull-right[ ## What is **next**: ### 1. Simulate outbreak ### 2. Predict time point of infection with centrality measure ### 3. Optimize prediction in a newly created measure ### 4. Test performance of new measure under different epidemic scenarios ]